Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.

On the Properties of Morphological Skeletons of Discrete Binary Image Using Double Structuring Elements

Abstract: This paper discusses the properties of the morphological skeletons of a discrete binary image using double structuring elements. Skeletons are generated under the main structuring element and the sub structuring element. The original image can be reconstructed using one of the structuring elements as the conventional skeleton by a single structuring element. Selecting a structuring element as the subset of other one, a characteristic pattern of the image can be generated as the skeleton subsets. Among them are a unique nested skeleton and a shape representing skeleton having skeletal line component and total shape one. The conventional type, skeleton can be obtained either by setting the two structuring be same or by the intersection of the two skeletons by two sets of double structuring elements. We discuss some theorems, control generation of the skeleton, and the related morphological skeletons with interesting examples

Mathematical Morphology (MM) Features for Classification of Cancerous Masses in Mammograms

Abstract: One of the important attributes of cancerous masses is their malignancy as it suggests a rapid growth of the cancer and possibility of metastasis. Malignancy, which denotes a special pathology of the tissue, is closely related to the existence of quasi-linear structures (spicules) emanating from the central mass. Hence, the tasks of malignancy and spicularity assessment are very often treated jointly. We propose a novel set of features enriching already existing pool of features for classification of masses. Our features are based on simple MM operations, pixel counting, and some basic algebra. To be more specific, given a contour of a cancerous mass we compute a sequence of dilations, and then count the number of pixels on the inner and the outer contour of each dilation. The contour pixel numbers are plotted against the size of the disk-shaped structuring element. The MM features are calculated from the plot via simple algebraic operations. The crucial point is that all the features are zero iff the input contour is circular. This distinctive property forms a basis for successful classification with the A z values higher than for the features existing in the literature. The additional advantage of our approach is the simplicity of the proposed features. In contrast to many features found in the literature, no sophisticated algorithms are employed, so reimplementation of the features should be easy for anyone interested.

Reference-free detection of semiconductor assembly defect

Abstract: This paper aims at developing a novel defect detection algorithm for the semiconductor assembly process by image analysis of a single captured image, without reference to another image during inspection The integrated circuit (IC) pattern is usually periodic and regular Therefore, we can implement a classification scheme whereby the regular pattern in the die image is classified as the acceptable circuit pattern and the die defect can be modeled as irregularity on the image The detection of irregularity in image is thus equivalent to the detection of die defect We propose a method where the defect detection algorithm first segments the die image into different regions according to the circuit pattern by a set of morphological segmentations with different structuring element sizes Then, a feature vector, which consists of many image attributes, is calculated for each segmented region Lastly, the defective region is extracted by the feature vector classification

Fast algorithm for size analysis of irregular pore areas

Abstract: Characterizing highly irregular 2D pore images requires special image analysis methods. An opening operation with a small circular structuring element has been used to remove small pores and small features of large pores from an image. Through a series of openings with increasingly larger structuring elements, all pore pixels are gradually removed according to the sizes of associated features. A pore size distribution is obtained as a result of this process. However, the conventional opening algorithm is very slow in performing this size analysis due to its iterative character. A direct approach algorithm has been developed to map size values for all pore pixels without iterative steps. There are three major steps in this algorithm. First, a distance map is constructed in which each pixel has a value equal to the distance between the pixel and the nearest pore boundary. Second, local maxima on the distance map are found. Finally, for each local maximum, a circular area is scanned around the pixel with a radius equal to the pixel value and all pixels within the circle are assigned the same value (as the local maximum). One pixel may be assigned several values from different local maxima; in such a case, the largest value should be chosen. The result is a size map and the histogram of this size map represents the pore size distribution. This analysis can be applied to any binary image.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Spectral morphology for feature extraction from multi- and hyper-spectral imagery

Abstract: For accurate and robust analysis of remotely-sensed imagery it is necessary to combine the information from both spectral and spatial domains in a meaningful manner. The two domains are intimately linked: objects in a scene are defined in terms of both their composition and their spatial arrangement, and cannot accurately be described by information from either of these two domains on their own. To date there have been relatively few methods for combining spectral and spatial information concurrently. Most techniques involve separate processing for extracting spatial and spectral information. In this paper we will describe several extensions to traditional morphological operators that can treat spectral and spatial domains concurrently and can be used to extract relationships between these domains in a meaningful way. This includes the investgation and development of suitable vector-ordering metrics and machine-learning-based techniques for optimizing the various parameters of the morphological operators, such as morphological operator, structuring element and vector ordering metric. We demonstrate their application to a range of multi- and hyper-spectral image analysis problems.